Triple

T8132500
Position Surface form Disambiguated ID Type / Status
Subject Las Américas International Airport E189884 entity
Predicate hubFor P423 FINISHED
Object Arajet
Arajet is a Dominican low-cost airline based in Santo Domingo that operates flights across the Caribbean and the Americas.
 E714021 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Arajet | Statement: [Las Américas International Airport, hubFor, Arajet]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Arajet
Context triple: [Las Américas International Airport, hubFor, Arajet]
  • A. Orneta
    Orneta is a small historic town in northern Poland known for its medieval architecture and location within the picturesque Warmian-Masurian region.
  • B. Avion
    Avion is a commune in the Pas-de-Calais department in northern France.
  • C. AeroGaviota
    AeroGaviota is a Cuban airline that primarily serves domestic and tourist-oriented routes, often connecting major hubs like Havana with resort and regional destinations across the country.
  • D. Air Astra
    Air Astra is a Bangladeshi airline that operates domestic flights, primarily centered around Dhaka’s Hazrat Shahjalal International Airport.
  • E. IrAero
    IrAero is a Russian regional airline that operates passenger and cargo flights across Siberia, the Russian Far East, and neighboring countries.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Arajet
Triple: [Las Américas International Airport, hubFor, Arajet]
Generated description
Arajet is a Dominican low-cost airline based in Santo Domingo that operates flights across the Caribbean and the Americas.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Arajet
Target entity description: Arajet is a Dominican low-cost airline based in Santo Domingo that operates flights across the Caribbean and the Americas.
  • A. Orneta
    Orneta is a small historic town in northern Poland known for its medieval architecture and location within the picturesque Warmian-Masurian region.
  • B. Avion
    Avion is a commune in the Pas-de-Calais department in northern France.
  • C. AeroGaviota
    AeroGaviota is a Cuban airline that primarily serves domestic and tourist-oriented routes, often connecting major hubs like Havana with resort and regional destinations across the country.
  • D. Air Astra
    Air Astra is a Bangladeshi airline that operates domestic flights, primarily centered around Dhaka’s Hazrat Shahjalal International Airport.
  • E. IrAero
    IrAero is a Russian regional airline that operates passenger and cargo flights across Siberia, the Russian Far East, and neighboring countries.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69ca82bcb4848190a9a9d036ad768642 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb43b96cd481908c0679050c35d83f completed March 31, 2026, 3:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc9482113881909439c9e43fbc933f completed April 1, 2026, 3:44 a.m.
NEDg Description generation batch_69cc95c180188190a2d541e8ea9a4c57 completed April 1, 2026, 3:49 a.m.
NED2 Entity disambiguation (via description) batch_69cc970698f88190a0869515904e50e3 completed April 1, 2026, 3:54 a.m.
Created at: March 30, 2026, 5:35 p.m.